On basis of QPIC [1]} structure, we define several variants for baselines by applying different combinations of sub-components from MSTR: multi-scale feature maps (MS), Deformable Attention (DA), Dual-Entity attention (DE), and Entity-conditioned Context attention (EC).
Specifically, since deformable attention can be also applied to a single-scale feature map, SS-Baseline denotes QPIC where the attention in the transformer is replaced by DA.
Our work can be seen as a process of improving the score to the state-of-the-art by adapting MS, DE, EC step by step to SS-Baseline.
MS-Baseline+DE+EC represents MSTR without merging with self-attention, instead simply passing the sum of the outputs to the next decoder layer.
